Hierarchical Blending with Transformer for Infrared and Visible Image Integration

Introduction: Infrared-visible (IR-VIS) image blending aims to synthesize a comprehensive representation by integrating complementary information from heterogeneous modalities. This research addresses the limitations of existing deep learning methods in harmonizing the extraction of multi-receptive-field representations, modeling long-short range dependencies, and detail- preserving recovery within a unified framework for IR-VIS image blending. Methods: We propose HBFormer, a novel hierarchical Transformer-based architecture. Key methodological innovations include: 1) the Dense Multi-Receptive-Field Block (DMRFB) with Ghost-Shuffle convolution for efficient multi-receptive-field representation abstraction, 2) the Bidirectional Attention Cooperation Block (BACB) for parallel short-long range dependency modeling, and 3) the Hierarchical Pooling Tokenization Block (HPTB) for computationally efficient multi-resolution integration. Additionally, a Detail-aware Perceptual Blending Loss (DPBLoss) was designed to enforce edge sharpness, semantic alignment, and modality-specific contrast. The proposed method was evaluated on the TNO and MSRS benchmark datasets. Results: Extensive exper